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JOINT KEYNOTE
E D E N Z O L L E RPrincipal Analyst, Consumer Services | Ovum
NAVIGATING THE AI JOURNEY: THE OVUM-AMDOCS AI MATURITY ASSESSMENT MODEL
M A T T H E W D O W L I N GHead of Marketing, Western Europe | Amdocs
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AI has the potential to bring benefits across the businessBut service providers are struggling to navigate the AI, or to even know where to start
(Big) data extract transform load (ETL), real-time methods, multi-granular data
Virtual e-care assistants & chat bots
AI agent guided assistance
Automatic self-healing processes
Predictive, self-serve support
Network orchestrationTraffic classificationAnomaly detection
Prediction (e.g. capacity planning, congestion)
Risk assessment
Product bundling recommendationsUpsell / cross-sell
SegmentationPersonality detection
AI assistantsSmart home
Connected carsMedia/entertainment
CommunicationsCommerce
Vertical markets (e.g. health, security)
Detection of anomalies in usage, buying
behavior etcDetection of
unregistered SIMsIdentity verification &
management Payments authentication
Humans in AI roles (Assistance, Data & AI scientists,
Network engineers etc) AI Systems
Customer Support
Service Innovation
Sales & Marketing
NetworkOptimization
Cyber security & fraud mitigation
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Virtual e-care assistants & chat bots
AI agent guided assistance
Automatic self-healing processes
Predictive, self-serve support
Network orchestrationTraffic classificationAnomaly detection
Prediction (e.g. capacity planning, congestion)
Risk assessment
Product bundling recommendationsUpsell / cross-sell
SegmentationPersonality detection
AI assistantsSmart home
Connected carsMedia/entertainment
CommunicationsCommerce
Vertical markets (e.g. health, security)
Detection of anomalies in usage, buying
behavior etcDetection of
unregistered SIMsIdentity verification &
management Payments authentication
Customer Support
Service Innovation
Sales & Marketing
NetworkOptimization
Cyber security & fraud mitigation
AI has the potential to bring benefits across the businessBut service providers are struggling to navigate the AI, or to even know where to start
Product bundle reccommendation
Cross-sell/Up-sell
Segmentation
Personality detection
Sales & Marketing
RevenueLift
Churn Reduction
App Downloads (1st time users)
App Re-Engagement
+2% -20% X2+15%NRR
Impacts of Intelligence…
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AI has the potential to bring benefits across the businessBut service providers are struggling to navigate the AI, or to even know where to start
(Big) data extract transform load (ETL), real-time methods, multi-granular data
Virtual e-care assistants & chat bots
AI agent guided assistance
Automatic self-healing processes
Predictive, self-serve support
Network orchestrationTraffic classificationAnomaly detection
Prediction (e.g. capacity planning, congestion)
Risk assessment
Product bundling recommendationsUpsell / cross-sell
SegmentationPersonality detection
AI assistantsSmart home
Connected carsMedia/entertainment
CommunicationsCommerce
Vertical markets (e.g. health, security)
Detection of anomalies in usage, buying
behavior etcDetection of
unregistered SIMsIdentity verification &
management Payments authentication
Humans in AI roles (Assistance, Data & AI scientists,
Network engineers etc) AI Systems
Customer Support
Service Innovation
Sales & Marketing
NetworkOptimization
Cyber security & fraud mitigation
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AI maturity must be assessed across multiple pillars – and in a joined up way
• How far and how AI is
being…
• Harnessed for network
optimization
• used to support fraud
detection
• Enhance customer care
scenarios (e.g. predictive
modelling to preempt )
• Enhance sales and
marketing engagement
(e.g. product catalogue
optimization)
• Used in specific B2B
scenarios
• The stage and state of AI
strategy – its
completeness/
comprehensiveness
• How AI fits into a wider
digital transformation
strategy
• AI budgets
consideration/decisions
• How AI will be
deployed/implemented
• How far operational use
cases for AI are identified
and understood
• Agreed metrics/KPIs to
measure the success of AI
• Degree of senior
management/board support
and sponsorship
• Level of understanding and
acceptance of AI in the
wider organization
• Structural support for AI
within the organization
• Internal skills available to
support AI
• Education
programs/initiatives to
address culture issues
related to AI
• The state of the data
processes and governance
within the business
• Current data and
information architecture
• Data model, standards, and
types
• Data availability for AI
solutions
• Data governance and
quality capabilities
• Enterprise analytics
platform and analytic
capabilities
• The types of AI
technologies and
capabilities in use/planned
• Stage of AI implementation
– Internal view (AI systems
for internal processes)
• Stage of AI implementation
– External view (AI for
enterprise or consumer
interactions)
• How AI has been
implemented (in-house,
with partners, both; on
premises, in the cloud,
hosted/manged service)
• Degree of AI integration
OperationsStrategy Organization Data Technology
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AI Maturity Model – Understanding the Data Pillar
Increased Telesales Numbers
8.5% Online Sales Conversion
+12 Points TNPS
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AI Maturity Model – Understanding the Operations Pillar
Proactive Care
Agent Assistance
Self-Service
Care Operations
↓ Calls
↑ Efficiency
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Positioning on the path to AI maturity
Level of AI competency
AI
matu
rity
ph
ase
Has not taken proactive steps on the AI journey and at best is in assessment mode
Sufficiently prepared in terms of strategy, organizational set-up, and data availability to implement AI
A reasonable degree of practical experience and an understanding of how to move forward with AI. But there are still gaps and limitations
A good level AI expertise and experience, with a proven track record across a range of use cases
AI Novice
AI Ready
AI Proficient
AI Advanced
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Strategy
Organization
Data
Technology
Operations
AI Ready AI Proficient AI AdvancedAI Novice
Positioning on the path to AI maturity – Scoring Composition
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Positioning on the path to AI maturity – Industry Benchmarking
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How to move forward on the AI journey
AI Ready
Basic, short-term AI strategy. Implementations based on point solutions with no central program. The AI strategy is supported by existing budgets.
AI Proficient
Good progress with AI technologies that are likely connected to a centralized AI platform. Strong partners with AI technical expertise.
AI Proficient
Data is generally available for AI use cases. Analytics likely focused on a central platform, but may not have advanced predictive and other data science capabilities.
.
AI Proficient
AI powering a good range of internal processes. External use cases focused on less challenging B2C scenarios in customer care, sales/marketing functions.
Strategy Data Technology Operations
Develop the basic, short-term tactical plan into a fully formed, longer term AI road map.
Build solutions from components that are open and easily integrated. Create a central framework flexible enough for the rapid development of new use cases.
Enhance analytics platform(s) with predictive and other data science tools. Ensure data is at the center of decision-making in day-to-day processes.
Build on what you know –both in terms of what has worked and what has not. Look at further B2C opportunities and also at making moves with B2B scenarios.
AI Ready
Senior support for AI, but not across all department heads. Little understanding of AI in wider organization. Foundation skills via data scientists, but no AI experts.
Organization
Foster wider buy in across all department. Address the AI skills gap.
AI Ready
Senior support for AI, but not across all department heads. Little understanding of AI in wider organization. Foundation skills via data scientists, but no AI experts.
Organization
Foster wider buy in across all department. Address the AI skills gap.
AI Proficient
Next steps
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So what does an AI Advanced service provider look like?
✓Dedicated budget in place and AI driving digital transformation and shaping new services and
capabilities
✓Metrics in place for AI specific use cases
Strategy
✓AI is championed and supported at the highest level (board, senior management, and
departmental/operational heads)
✓Understood across the organization and perceived positivelyOrganization
✓Multiple sources of data available in (or close to) real time, even for some of the most demanding
use cases
✓Data science is an integrated part of analytics within the CSPData
✓AI technologies are already adopted and are at the approved project stage or being deployed.
Also likely to be integrated into the CSP's existing systems and have AI capabilities infused
✓Well-established relationships with AI expert partnersAI AdvancedTechnology
✓ Implemented AI to power a range of internal processes, external B2C use cases, and certain B2B
scenarios
✓AI initiatives are developed with privacy at their core from the start, not as an afterthoughtOperations
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Thank-you
For more information…
To take our Online Assessment, please email: [email protected]
To view the Ovum Amdocs AI Maturity Model, go to: https://www.amdocs.com/sites/default/files/filefield_paths/ai-maturity-model-whitepaper.pdf